Overview

Dataset statistics

Number of variables15
Number of observations1232
Missing cells3600
Missing cells (%)19.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory144.5 KiB
Average record size in memory120.1 B

Variable types

Categorical4
Numeric11

Alerts

Year is highly overall correlated with SLGHigh correlation
RS is highly overall correlated with W and 3 other fieldsHigh correlation
RA is highly overall correlated with W and 2 other fieldsHigh correlation
W is highly overall correlated with RS and 5 other fieldsHigh correlation
OBP is highly overall correlated with RS and 2 other fieldsHigh correlation
SLG is highly overall correlated with Year and 3 other fieldsHigh correlation
BA is highly overall correlated with RS and 2 other fieldsHigh correlation
RankSeason is highly overall correlated with W and 1 other fieldsHigh correlation
OOBP is highly overall correlated with RA and 2 other fieldsHigh correlation
OSLG is highly overall correlated with RA and 2 other fieldsHigh correlation
Team is highly overall correlated with LeagueHigh correlation
League is highly overall correlated with TeamHigh correlation
Playoffs is highly overall correlated with W and 2 other fieldsHigh correlation
RankPlayoffs is highly overall correlated with PlayoffsHigh correlation
RankSeason has 988 (80.2%) missing valuesMissing
RankPlayoffs has 988 (80.2%) missing valuesMissing
OOBP has 812 (65.9%) missing valuesMissing
OSLG has 812 (65.9%) missing valuesMissing
League is uniformly distributedUniform

Reproduction

Analysis started2023-02-02 03:37:00.424674
Analysis finished2023-02-02 03:37:23.165525
Duration22.74 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Team
Categorical

Distinct39
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
CLE
 
47
BAL
 
47
HOU
 
47
CHC
 
47
BOS
 
47
Other values (34)
997 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3696
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowARI
2nd rowATL
3rd rowBAL
4th rowBOS
5th rowCHC

Common Values

ValueCountFrequency (%)
CLE 47
 
3.8%
BAL 47
 
3.8%
HOU 47
 
3.8%
CHC 47
 
3.8%
BOS 47
 
3.8%
LAD 47
 
3.8%
DET 47
 
3.8%
PIT 47
 
3.8%
CIN 47
 
3.8%
CHW 47
 
3.8%
Other values (29) 762
61.9%

Length

2023-02-01T21:37:23.270247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cle 47
 
3.8%
chw 47
 
3.8%
sfg 47
 
3.8%
nym 47
 
3.8%
phi 47
 
3.8%
stl 47
 
3.8%
nyy 47
 
3.8%
min 47
 
3.8%
cin 47
 
3.8%
pit 47
 
3.8%
Other values (29) 762
61.9%

Most occurring characters

ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3696
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 3696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

League
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
AL
616 
NL
616 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2464
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNL
2nd rowNL
3rd rowAL
4th rowAL
5th rowNL

Common Values

ValueCountFrequency (%)
AL 616
50.0%
NL 616
50.0%

Length

2023-02-01T21:37:23.405883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-01T21:37:23.541521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nl 616
50.0%
al 616
50.0%

Most occurring characters

ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2464
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Year
Real number (ℝ)

Distinct47
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.9578
Minimum1962
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:23.666188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1965
Q11976.75
median1989
Q32002
95-th percentile2010
Maximum2012
Range50
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation14.819625
Coefficient of variation (CV)0.00745095
Kurtosis-1.2048462
Mean1988.9578
Median Absolute Deviation (MAD)13
Skewness-0.15192926
Sum2450396
Variance219.62129
MonotonicityDecreasing
2023-02-01T21:37:23.827755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2012 30
 
2.4%
2004 30
 
2.4%
2011 30
 
2.4%
1998 30
 
2.4%
1999 30
 
2.4%
2001 30
 
2.4%
2002 30
 
2.4%
2003 30
 
2.4%
2000 30
 
2.4%
2005 30
 
2.4%
Other values (37) 932
75.6%
ValueCountFrequency (%)
1962 20
1.6%
1963 20
1.6%
1964 20
1.6%
1965 20
1.6%
1966 20
1.6%
1967 20
1.6%
1968 20
1.6%
1969 24
1.9%
1970 24
1.9%
1971 24
1.9%
ValueCountFrequency (%)
2012 30
2.4%
2011 30
2.4%
2010 30
2.4%
2009 30
2.4%
2008 30
2.4%
2007 30
2.4%
2006 30
2.4%
2005 30
2.4%
2004 30
2.4%
2003 30
2.4%

RS
Real number (ℝ)

Distinct374
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.08198
Minimum463
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:23.990360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum463
5-th percentile570.55
Q1652
median711
Q3775
95-th percentile871.45
Maximum1009
Range546
Interquartile range (IQR)123

Descriptive statistics

Standard deviation91.534294
Coefficient of variation (CV)0.12800531
Kurtosis-0.020576521
Mean715.08198
Median Absolute Deviation (MAD)61
Skewness0.17450786
Sum880981
Variance8378.527
MonotonicityNot monotonic
2023-02-01T21:37:24.141917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 11
 
0.9%
682 11
 
0.9%
758 10
 
0.8%
735 10
 
0.8%
708 10
 
0.8%
707 10
 
0.8%
673 9
 
0.7%
686 9
 
0.7%
714 9
 
0.7%
731 9
 
0.7%
Other values (364) 1134
92.0%
ValueCountFrequency (%)
463 1
0.1%
464 1
0.1%
468 1
0.1%
470 1
0.1%
473 1
0.1%
486 1
0.1%
495 2
0.2%
498 2
0.2%
501 1
0.1%
510 1
0.1%
ValueCountFrequency (%)
1009 1
0.1%
993 1
0.1%
978 1
0.1%
968 2
0.2%
965 1
0.1%
961 2
0.2%
952 1
0.1%
950 1
0.1%
949 2
0.2%
947 1
0.1%

RA
Real number (ℝ)

Distinct381
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.08198
Minimum472
Maximum1103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:24.470081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum472
5-th percentile571.1
Q1649.75
median709
Q3774.25
95-th percentile877.8
Maximum1103
Range631
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation93.079933
Coefficient of variation (CV)0.1301668
Kurtosis-0.010927035
Mean715.08198
Median Absolute Deviation (MAD)62
Skewness0.29856263
Sum880981
Variance8663.8739
MonotonicityNot monotonic
2023-02-01T21:37:24.616649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
717 11
 
0.9%
744 10
 
0.8%
657 10
 
0.8%
680 10
 
0.8%
648 10
 
0.8%
731 10
 
0.8%
611 9
 
0.7%
745 9
 
0.7%
643 9
 
0.7%
698 9
 
0.7%
Other values (371) 1135
92.1%
ValueCountFrequency (%)
472 1
0.1%
490 1
0.1%
491 1
0.1%
492 1
0.1%
497 1
0.1%
499 1
0.1%
501 1
0.1%
504 1
0.1%
509 1
0.1%
517 2
0.2%
ValueCountFrequency (%)
1103 1
0.1%
1028 1
0.1%
974 1
0.1%
971 1
0.1%
969 1
0.1%
968 1
0.1%
967 2
0.2%
964 1
0.1%
957 1
0.1%
948 1
0.1%

W
Real number (ℝ)

Distinct63
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.904221
Minimum40
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:24.779212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62
Q173
median81
Q389
95-th percentile98
Maximum116
Range76
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.458139
Coefficient of variation (CV)0.14162597
Kurtosis-0.29902183
Mean80.904221
Median Absolute Deviation (MAD)8
Skewness-0.18186642
Sum99674
Variance131.28895
MonotonicityNot monotonic
2023-02-01T21:37:24.929812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 49
 
4.0%
86 46
 
3.7%
79 43
 
3.5%
76 43
 
3.5%
88 43
 
3.5%
90 40
 
3.2%
89 39
 
3.2%
75 39
 
3.2%
85 38
 
3.1%
80 37
 
3.0%
Other values (53) 815
66.2%
ValueCountFrequency (%)
40 1
 
0.1%
43 1
 
0.1%
50 1
 
0.1%
51 2
 
0.2%
52 2
 
0.2%
53 3
0.2%
54 5
0.4%
55 4
0.3%
56 6
0.5%
57 6
0.5%
ValueCountFrequency (%)
116 1
 
0.1%
114 1
 
0.1%
109 1
 
0.1%
108 3
 
0.2%
106 1
 
0.1%
105 1
 
0.1%
104 4
 
0.3%
103 9
0.7%
102 10
0.8%
101 11
0.9%

OBP
Real number (ℝ)

Distinct87
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32633117
Minimum0.277
Maximum0.373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:25.099357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.277
5-th percentile0.302
Q10.317
median0.326
Q30.337
95-th percentile0.352
Maximum0.373
Range0.096
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.015012772
Coefficient of variation (CV)0.046004715
Kurtosis0.067597287
Mean0.32633117
Median Absolute Deviation (MAD)0.01
Skewness0.017635262
Sum402.04
Variance0.00022538333
MonotonicityNot monotonic
2023-02-01T21:37:25.261922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.322 42
 
3.4%
0.32 39
 
3.2%
0.325 37
 
3.0%
0.321 36
 
2.9%
0.333 35
 
2.8%
0.33 34
 
2.8%
0.324 34
 
2.8%
0.323 33
 
2.7%
0.331 33
 
2.7%
0.329 31
 
2.5%
Other values (77) 878
71.3%
ValueCountFrequency (%)
0.277 1
 
0.1%
0.281 1
 
0.1%
0.283 1
 
0.1%
0.284 1
 
0.1%
0.285 3
0.2%
0.287 1
 
0.1%
0.288 2
0.2%
0.289 1
 
0.1%
0.29 1
 
0.1%
0.291 3
0.2%
ValueCountFrequency (%)
0.373 1
 
0.1%
0.369 1
 
0.1%
0.367 1
 
0.1%
0.366 3
0.2%
0.364 1
 
0.1%
0.363 2
 
0.2%
0.362 6
0.5%
0.361 3
0.2%
0.36 6
0.5%
0.359 1
 
0.1%

SLG
Real number (ℝ)

Distinct162
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39734172
Minimum0.301
Maximum0.491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:25.432511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile0.344
Q10.375
median0.396
Q30.421
95-th percentile0.455
Maximum0.491
Range0.19
Interquartile range (IQR)0.046

Descriptive statistics

Standard deviation0.033266899
Coefficient of variation (CV)0.083723649
Kurtosis-0.31717178
Mean0.39734172
Median Absolute Deviation (MAD)0.023
Skewness0.054330043
Sum489.525
Variance0.0011066865
MonotonicityNot monotonic
2023-02-01T21:37:25.600019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.401 21
 
1.7%
0.381 20
 
1.6%
0.395 20
 
1.6%
0.409 19
 
1.5%
0.391 19
 
1.5%
0.403 18
 
1.5%
0.388 18
 
1.5%
0.396 18
 
1.5%
0.385 17
 
1.4%
0.387 17
 
1.4%
Other values (152) 1045
84.8%
ValueCountFrequency (%)
0.301 1
 
0.1%
0.311 1
 
0.1%
0.315 3
0.2%
0.317 2
0.2%
0.318 3
0.2%
0.319 2
0.2%
0.32 1
 
0.1%
0.325 1
 
0.1%
0.326 2
0.2%
0.327 3
0.2%
ValueCountFrequency (%)
0.491 1
 
0.1%
0.485 1
 
0.1%
0.484 1
 
0.1%
0.483 1
 
0.1%
0.479 1
 
0.1%
0.478 2
 
0.2%
0.477 1
 
0.1%
0.475 2
 
0.2%
0.472 6
0.5%
0.471 1
 
0.1%

BA
Real number (ℝ)

Distinct75
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25927273
Minimum0.214
Maximum0.294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:25.774554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.214
5-th percentile0.238
Q10.251
median0.26
Q30.268
95-th percentile0.28
Maximum0.294
Range0.08
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012907229
Coefficient of variation (CV)0.04978244
Kurtosis0.0095668343
Mean0.25927273
Median Absolute Deviation (MAD)0.009
Skewness-0.11118461
Sum319.424
Variance0.00016659656
MonotonicityNot monotonic
2023-02-01T21:37:25.929139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.263 49
 
4.0%
0.261 44
 
3.6%
0.258 41
 
3.3%
0.264 40
 
3.2%
0.256 39
 
3.2%
0.26 38
 
3.1%
0.259 36
 
2.9%
0.267 36
 
2.9%
0.262 36
 
2.9%
0.27 35
 
2.8%
Other values (65) 838
68.0%
ValueCountFrequency (%)
0.214 1
 
0.1%
0.219 1
 
0.1%
0.22 1
 
0.1%
0.221 1
 
0.1%
0.223 1
 
0.1%
0.224 1
 
0.1%
0.225 4
0.3%
0.227 2
0.2%
0.228 3
0.2%
0.229 3
0.2%
ValueCountFrequency (%)
0.294 1
 
0.1%
0.293 2
 
0.2%
0.292 1
 
0.1%
0.291 2
 
0.2%
0.29 1
 
0.1%
0.289 3
 
0.2%
0.288 8
0.6%
0.287 6
0.5%
0.286 3
 
0.2%
0.285 3
 
0.2%

Playoffs
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
0
988 
1
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1232
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Length

2023-02-01T21:37:26.094698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-01T21:37:26.230334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1232
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

RankSeason
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)3.3%
Missing988
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean3.1229508
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:26.336051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7383492
Coefficient of variation (CV)0.55663675
Kurtosis-0.54447572
Mean3.1229508
Median Absolute Deviation (MAD)1
Skewness0.56293748
Sum762
Variance3.0218579
MonotonicityNot monotonic
2023-02-01T21:37:26.493630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 53
 
4.3%
1 52
 
4.2%
3 44
 
3.6%
4 44
 
3.6%
5 21
 
1.7%
6 20
 
1.6%
7 9
 
0.7%
8 1
 
0.1%
(Missing) 988
80.2%
ValueCountFrequency (%)
1 52
4.2%
2 53
4.3%
3 44
3.6%
4 44
3.6%
5 21
 
1.7%
6 20
 
1.6%
7 9
 
0.7%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
7 9
 
0.7%
6 20
 
1.6%
5 21
 
1.7%
4 44
3.6%
3 44
3.6%
2 53
4.3%
1 52
4.2%

RankPlayoffs
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)2.0%
Missing988
Missing (%)80.2%
Memory size9.8 KiB
3.0
80 
4.0
68 
2.0
47 
1.0
47 
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters732
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 80
 
6.5%
4.0 68
 
5.5%
2.0 47
 
3.8%
1.0 47
 
3.8%
5.0 2
 
0.2%
(Missing) 988
80.2%

Length

2023-02-01T21:37:26.750943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-01T21:37:26.949412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 80
32.8%
4.0 68
27.9%
1.0 47
19.3%
2.0 47
19.3%
5.0 2
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 244
33.3%
. 244
33.3%
3 80
 
10.9%
4 68
 
9.3%
1 47
 
6.4%
2 47
 
6.4%
5 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 488
66.7%
Other Punctuation 244
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
50.0%
3 80
 
16.4%
4 68
 
13.9%
1 47
 
9.6%
2 47
 
9.6%
5 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244
33.3%
. 244
33.3%
3 80
 
10.9%
4 68
 
9.3%
1 47
 
6.4%
2 47
 
6.4%
5 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244
33.3%
. 244
33.3%
3 80
 
10.9%
4 68
 
9.3%
1 47
 
6.4%
2 47
 
6.4%
5 2
 
0.3%

G
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.91883
Minimum158
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:27.100010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile161
Q1162
median162
Q3162
95-th percentile163
Maximum165
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62436523
Coefficient of variation (CV)0.0038560384
Kurtosis7.0242506
Mean161.91883
Median Absolute Deviation (MAD)0
Skewness-1.0446305
Sum199484
Variance0.38983194
MonotonicityNot monotonic
2023-02-01T21:37:27.210713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
162 954
77.4%
161 139
 
11.3%
163 93
 
7.5%
160 23
 
1.9%
164 10
 
0.8%
159 10
 
0.8%
165 2
 
0.2%
158 1
 
0.1%
ValueCountFrequency (%)
158 1
 
0.1%
159 10
 
0.8%
160 23
 
1.9%
161 139
 
11.3%
162 954
77.4%
163 93
 
7.5%
164 10
 
0.8%
165 2
 
0.2%
ValueCountFrequency (%)
165 2
 
0.2%
164 10
 
0.8%
163 93
 
7.5%
162 954
77.4%
161 139
 
11.3%
160 23
 
1.9%
159 10
 
0.8%
158 1
 
0.1%

OOBP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)17.1%
Missing812
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean0.33226429
Minimum0.294
Maximum0.384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:27.365300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.294
5-th percentile0.30895
Q10.321
median0.331
Q30.343
95-th percentile0.35705
Maximum0.384
Range0.09
Interquartile range (IQR)0.022

Descriptive statistics

Standard deviation0.015295316
Coefficient of variation (CV)0.046033584
Kurtosis-0.3483253
Mean0.33226429
Median Absolute Deviation (MAD)0.011
Skewness0.19574296
Sum139.551
Variance0.00023394669
MonotonicityNot monotonic
2023-02-01T21:37:27.530857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.314 13
 
1.1%
0.327 13
 
1.1%
0.329 13
 
1.1%
0.33 12
 
1.0%
0.342 12
 
1.0%
0.336 12
 
1.0%
0.338 11
 
0.9%
0.323 11
 
0.9%
0.319 11
 
0.9%
0.328 11
 
0.9%
Other values (62) 301
 
24.4%
(Missing) 812
65.9%
ValueCountFrequency (%)
0.294 1
 
0.1%
0.296 1
 
0.1%
0.301 1
 
0.1%
0.302 1
 
0.1%
0.303 2
 
0.2%
0.304 1
 
0.1%
0.305 2
 
0.2%
0.306 4
0.3%
0.307 2
 
0.2%
0.308 6
0.5%
ValueCountFrequency (%)
0.384 1
 
0.1%
0.372 1
 
0.1%
0.371 1
 
0.1%
0.369 1
 
0.1%
0.368 1
 
0.1%
0.367 1
 
0.1%
0.365 1
 
0.1%
0.364 1
 
0.1%
0.362 6
0.5%
0.361 2
 
0.2%

OSLG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)26.7%
Missing812
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean0.41974286
Minimum0.346
Maximum0.499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.8 KiB
2023-02-01T21:37:27.698411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.346
5-th percentile0.37795
Q10.401
median0.419
Q30.438
95-th percentile0.46405
Maximum0.499
Range0.153
Interquartile range (IQR)0.037

Descriptive statistics

Standard deviation0.026509611
Coefficient of variation (CV)0.06315679
Kurtosis-0.18958337
Mean0.41974286
Median Absolute Deviation (MAD)0.018
Skewness0.11846451
Sum176.292
Variance0.0007027595
MonotonicityNot monotonic
2023-02-01T21:37:27.855988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.431 16
 
1.3%
0.423 13
 
1.1%
0.422 10
 
0.8%
0.398 9
 
0.7%
0.407 8
 
0.6%
0.404 8
 
0.6%
0.415 8
 
0.6%
0.408 8
 
0.6%
0.405 8
 
0.6%
0.397 7
 
0.6%
Other values (102) 325
26.4%
(Missing) 812
65.9%
ValueCountFrequency (%)
0.346 1
 
0.1%
0.352 1
 
0.1%
0.354 1
 
0.1%
0.361 3
0.2%
0.364 2
0.2%
0.368 1
 
0.1%
0.37 1
 
0.1%
0.371 1
 
0.1%
0.372 3
0.2%
0.373 1
 
0.1%
ValueCountFrequency (%)
0.499 1
 
0.1%
0.494 1
 
0.1%
0.483 1
 
0.1%
0.481 1
 
0.1%
0.48 1
 
0.1%
0.476 4
0.3%
0.475 1
 
0.1%
0.474 1
 
0.1%
0.473 1
 
0.1%
0.471 2
0.2%

Interactions

2023-02-01T21:37:20.474831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:01.655303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.511331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.558695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.314001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.142115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.984191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.900109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.770071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:16.652050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.782355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:20.652357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:01.812170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.665919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.716274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.472577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.286728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:11.150745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.069616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.919671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:16.860493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.930960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:20.824895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:01.973500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.815639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.871895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.631154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.435332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:11.315346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.236171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.060296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:17.096861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.074619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:20.985468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:02.143373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.977275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.033426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.791724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.586925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:11.494825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.408712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.201060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:17.293337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.212206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:21.167979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:02.312118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:04.140838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.202971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.962268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.745502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:11.672352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.586237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.348627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:17.500781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.367790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:21.364454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:02.464582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:04.617368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.353569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.127826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:09.988853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:11.833920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.744812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.509199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:17.671327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.543322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:21.553947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:02.646956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:04.784919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.533088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.310338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.158398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.023415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:13.932311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.668770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:17.854834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.701899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:21.723382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:02.845427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:04.951474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.705627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.490855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.322958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.210913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.119809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.817639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.037390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.857482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:21.867995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.014962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.095089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:06.854231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.644445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.487518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.374476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.274436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:15.974860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.212879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:19.996111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:22.057488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.186216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.253667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.022780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.814989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.641108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.552000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.453918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:16.137427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.403368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:20.159674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:22.218058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:03.330789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:05.392340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:07.154427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:08.963593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:10.800681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:12.701600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:14.599528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:16.296001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:18.591866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-02-01T21:37:20.298303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2023-02-01T21:37:28.029525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
YearRSRAWOBPSLGBARankSeasonGOOBPOSLGTeamLeaguePlayoffsRankPlayoffs
Year1.0000.4010.395-0.0030.3390.5320.3140.352-0.008-0.365-0.3400.1060.0000.1300.248
RS0.4011.0000.3450.5030.8840.9120.806-0.0600.0780.0860.1430.1530.1970.3880.109
RA0.3950.3451.000-0.5340.2990.4130.3000.387-0.0470.9130.9020.1620.1650.2420.158
W-0.0030.503-0.5341.0000.4680.3900.394-0.8150.123-0.651-0.5860.1350.0410.7320.000
OBP0.3390.8840.2990.4681.0000.7680.833-0.0190.0290.0740.1090.1700.1660.3720.129
SLG0.5320.9120.4130.3900.7681.0000.7660.1090.0390.0950.1470.1860.1560.3300.157
BA0.3140.8060.3000.3940.8330.7661.0000.0260.0420.1330.1750.2070.2100.2860.092
RankSeason0.352-0.0600.387-0.815-0.0190.1090.0261.0000.0690.1170.0400.1600.0671.0000.166
G-0.0080.078-0.0470.1230.0290.0390.0420.0691.000-0.080-0.0120.0000.0980.0450.000
OOBP-0.3650.0860.913-0.6510.0740.0950.1330.117-0.0801.0000.8370.1690.0000.4250.160
OSLG-0.3400.1430.902-0.5860.1090.1470.1750.040-0.0120.8371.0000.2080.1130.3740.000
Team0.1060.1530.1620.1350.1700.1860.2070.1600.0000.1690.2081.0000.9690.2200.000
League0.0000.1970.1650.0410.1660.1560.2100.0670.0980.0000.1130.9691.0000.0000.000
Playoffs0.1300.3880.2420.7320.3720.3300.2861.0000.0450.4250.3740.2200.0001.0001.000
RankPlayoffs0.2480.1090.1580.0000.1290.1570.0920.1660.0000.1600.0000.0000.0001.0001.000

Missing values

2023-02-01T21:37:22.479399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-01T21:37:22.796514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-01T21:37:23.023947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TeamLeagueYearRSRAWOBPSLGBAPlayoffsRankSeasonRankPlayoffsGOOBPOSLG
0ARINL2012734688810.3280.4180.2590NaNNaN1620.3170.415
1ATLNL2012700600940.3200.3890.24714.05.01620.3060.378
2BALAL2012712705930.3110.4170.24715.04.01620.3150.403
3BOSAL2012734806690.3150.4150.2600NaNNaN1620.3310.428
4CHCNL2012613759610.3020.3780.2400NaNNaN1620.3350.424
5CHWAL2012748676850.3180.4220.2550NaNNaN1620.3190.405
6CINNL2012669588970.3150.4110.25112.04.01620.3050.390
7CLEAL2012667845680.3240.3810.2510NaNNaN1620.3360.430
8COLNL2012758890640.3300.4360.2740NaNNaN1620.3570.470
9DETAL2012726670880.3350.4220.26816.02.01620.3140.402
TeamLeagueYearRSRAWOBPSLGBAPlayoffsRankSeasonRankPlayoffsGOOBPOSLG
1222LADNL19628426971020.3370.4000.2680NaNNaN165NaNNaN
1223MINAL1962798713910.3380.4120.2600NaNNaN163NaNNaN
1224MLNNL1962730665860.3260.4030.2520NaNNaN162NaNNaN
1225NYMNL1962617948400.3180.3610.2400NaNNaN161NaNNaN
1226NYYAL1962817680960.3370.4260.26712.01.0162NaNNaN
1227PHINL1962705759810.3300.3900.2600NaNNaN161NaNNaN
1228PITNL1962706626930.3210.3940.2680NaNNaN161NaNNaN
1229SFGNL19628786901030.3410.4410.27811.02.0165NaNNaN
1230STLNL1962774664840.3350.3940.2710NaNNaN163NaNNaN
1231WSAAL1962599716600.3080.3730.2500NaNNaN162NaNNaN